Runware MCP Server

A powerful Model Context Protocol (MCP) server that provides lightning fast image and video generation tools using the Runware API. This server supports both SSE (Server-Sent Events) transport for custom claude connector and direct claude desktop installation as well.

Features

Image Generation Tools

• imageInference: Full-featured image generation with advanced parameters

• photoMaker: Subject personalization with PhotoMaker technology

• imageUpscale: High-quality image resolution enhancement

• imageBackgroundRemoval: Background removal with multiple AI models

• imageCaption: AI-powered image description generation

• imageMasking: Automatic mask generation for faces, hands, and people

Video Generation Tools

• videoInference: Text-to-video and image-to-video generation

• listVideoModels: Discover available video models

• getVideoModelInfo: Get detailed model specifications

Utility Tools

• imageUpload: Upload local images to get Runware UUIDs

• modelSearch: Search and discover AI models on the platform

Smart Features

• Automatic Model Selection: I2V uses klingai:5@2, T2V uses google:3@1

• Input Validation: Prevents Claude upload URL pasting and validates dimensions

• Comprehensive Error Handling: Clear error messages and guidance

Related MCP server: Runware MCP Server

Demo

Watch the demo video to see the Runware MCP server in action:

https://github.com/user-attachments/assets/9732096b-8513-455c-9759-cc88363c42f9

Architecture

[ MCP Client / AI Assistant ]
           |
    (connects via SSE over HTTP)
           |
    [ Uvicorn Server ]
           |
    [ Starlette App ]
           |
    [ FastMCP Server ]
           |
    [ Runware API ]

• Transport: SSE (Server-Sent Events) for real-time communication

• Framework: FastMCP with Starlette web framework

• Server: Uvicorn ASGI server

• API: Direct integration with Runware's AI services

Prerequisites

• Python: 3.10 or higher

• Runware API Key: Get your API key from Runware Dashboard

• Dependencies: See requirements.txt or pyproject.toml

Installation

1. Clone the Repository

git clone https://github.com/Runware/MCP-Runware.git
cd MCP-Runware

2. Install Dependencies

# Using uv (recommended)
uv venv
source .venv/bin/activate
uv pip install .

# Or using pip
pip install -r requirements.txt

3. Environment Setup

Create a .env file in the project root:

RUNWARE_API_KEY=your_api_key_here

Deployment Methods

Method 1: SSE Server (Recommended for Production)

Docker Deployment

# Build the Docker image
docker build -t runware_mcp_sse .

# Run the container
docker run --rm -p 8081:8081 runware_mcp_sse

Method 2: MCP Install (Direct Integration)

Install in Claude Desktop

# From the project directory
mcp install --with-editable . runware_mcp_server.py

Model Recommendations

Image Generation

• Default: civitai:943001@1055701 (SDXL-based)

• PhotoMaker: civitai:139562@344487 (RealVisXL V4.0)

• Background Removal: runware:109@1 (RemBG 1.4)

Video Generation

• Image-to-Video (I2V): klingai:5@2 (1920x1080)

• Text-to-Video (T2V): google:3@1 (1280x720)

You can find all additional models here: Runware Models

Configuration

Environment Variables

• RUNWARE_API_KEY: Your Runware API key (required)

Input Validation

• Rejects Claude upload URLs (https://files.*). Claude tends to include base64 strings in its reasoning/thinking process, which rapidly fills the context window with garbage data. Learn more about this issue

• Supports local file paths, public accessible URLs (make sure it has proper file extension such as JPG, PNG, WEBP, etc), and Runware UUIDs

Support

• Documentation: Runware API Docs

• Models: Browse All Models

• Dashboard: Runware Dashboard

• Issues: Create an issue in this repository

• Email: support@runware.ai